Earthquake monitoring

ABSTRACT

In a method of monitoring parameters of changes in space and time of seismic activity at predetermined points of investigation and times thereof in a region of investigation, more particularly the forecasting of earthquakes, the location and time of quakes of relative low strength (microquakes) are sensed in said region being investigated and the seismic activity of the microquakes is quantified in time and space by means of at least one parameter (SEISMOLAP) at the point of investigation and at the times of investigation, a probability value is established for the occurence of the parameter, and the development of the probability value in time is monitored to detect SEISMOLAP anomalies by clusters of microquakes and/or phases of relative seismic inactivity as activities leading up to more severe quake occurences more accurately in location and more reliably.

DESCRIPTION

The invention relates to a method as set forth in the preamble of claim1 provided for monitoring the seismic activity parameters in a region ofinvestigation and more particularly to improve the possibilities ofpredicting (forecasting) earthquakes.

Hitherto it has not been possible to predict time, place (epicenter) andstrength (magnitude) of an anticipated earthquake with sufficientaccuracy so that corresponding safety precautions, more particularly forprotection of the population concerned, can be taken. Methods knownhitherto fail to exhibit the necessary reliability to justify settingoff an earthquake alarm and, for example, to instigate large-scaleevacuation measures.

A method by the name of SEISMOLAP (SEISMicOverLAPping) developed by thepresent applicant is known which permits a considerable improvement inpredicting earthquakes. This method is described in the two-year report1992/93 of the "GeoForschungszentrum Potsdam". The contents of the thispublication hereby is incorporated in the instant description.

The known SEISMOLAP method comprises recording and quantifying twophenomena, namely seismic clustering and seismic inactivity.

Seismic clustering is understood to be focussing minor microquakes intime and space on the point of the later severe earthquake occurence.This phenomen is quantified by forming the so-called SEISMOLAPparameter.

This SEISMOLAP parameter (S in the following) is established as follows(see FIG. 2).

In a region being investigated, provided with a network of investigationlocations or points, a table or a catalog of the microquakes havingoccured is compiled, listing the point in time of each quake, itslocation and its magnitude. By the known method each microquake isassigned a 2- to 4-dimensional body. In the simplest case this is formedby a square; when including the depth of the quake by a cube centered atthe epicenter and furthermore considering the time interval (timewindow) in which the observation occurs, by a 4-dimensionalconfiguration. The latter has the dimensional "km³ days", the time axisextending, however, only into the past and not into the future.

On the region being investigated a network of grid lines is formed, theintersections of which are assigned in each case a 2- to 4-dimensionalbody which, for instance, in the two-dimensional case is formed by asquare having the same size as the squares assigned to the microquakes.

Usually, the dimensions of the 2- to 4-dimensional bodies assigned tothe quakes or points of intersection are equal in size and, depending onthe concrete conditions, selected more particularly according to thestrength of the main quake sought for or anticipated.

The parameter S₁ results from the sum of all overlappings of the 2- to4-dimensional bodies, each of which is assigned to the quakes or pointof intersection (point of investigation) in accordance with equation(1). The two-dimensional case is illustrated in FIG. 2. The locations ofthe quakes are symbolized by asterisks. The areas denoted black identifythe overlappings, the sum of which produces the parameter S¹. ##EQU1##where dij and D₁ resp. is given by: ##EQU2## (X_(j),Y_(j),Z_(j)) are thecoordinates of the quake occurring at the point in time T_(j), whilst(X, Y, Z) are the coordinates of the point of investigation. D₁₋₃ =DXare the dimensions of the space window and D₄ =DT denotes the timewindow. With these parameters the "volume" of the 2- to 4-dimensionalbody is defined which in turn appears in equation (1) as the normalizingfactor.

Thus, whilst in the original version the SEISMOLAP method merelyestablished one measure of the spatial concentration of microquakes, inthe version known last also a temporal frequency was included inestablishing the SEISMOLAP parameter. For this purpose four-dimensionalconfigurations were superimposed which apart from the three spatialcoordinates also contained the time as a fourth dimension. This meanspractically that microquakes located far from a point investigated inthe earth's crust, or dating back far in time, make only a minorcontribution, or no contribution at all to the SEISMOLAP parameter for alocation investigated and a specific point in time. By contrast, thosewhich lie near to the point, i.e. for example within a space windowabout the location investigated, and not dating far back in time, i.e.for instance within a time window about the point in time concerned,make a major contribution. The denser the microquakes concentrate aboutthe crust location investigated and the more often they occur, thegreater is the SEISMOLAP parameter.

The SEISMOLAP parameter can be determined for a specific point ofinvestigation or for a broad region. Optionally the strength of themicroquake may also be weighted. The stronger the microquake is, thegreater is the corresponding four-dimensional configuration selected.

The effectiveness of this known method was demonstrated, among otherthings, by application ("postcasting") to the Sapanca quake (9.12.1988,M=4.2 on the open Richter scale). A few days prior to the quake theSEISMOLAP parameter at the epicenter strongly departed from the zeroline and signalized on the evening prior to the day of the quake thecoming occurence by a particularly strong increase (see FIG. 10 of theaforementioned two-year report 1992/93). This behaviour of the SEISMOLAPparameter in the direct time window of a major quake is termed aSEISMOLAP anomaly.

SEISMOLAP₋₋ 1 is a direct measure of seismic clustering, whereas theseismic inactivity is quantified by a reciprocal value of this parameter(SEISMOLAP₋₋ 2 or S₂), see equation (2),

    SEISMOLAP.sub.-- 2=S.sub.2 (X,Y,Z,T)=(S.sub.1).sup.-1      ( 2)

The possibility of being able to distinguish not only phases of activitybut also phases of inactivity was thus found by considering thereciprocal value of SEISMOLAP₋₋ 1.

As an alternative to equation (2) the reciprocal value of SEISMOLAP₋₋ 1may be formed with respect to a larger window in space about the pointof investigation. This is indicated by the index GR of the parameter S₁.

The linear combination of both parameters S₁ and S₂ :

    AS.sub.1 -B1/S.sub.1,GR =AS.sub.1 -BS.sub.2,GR

permits a gradual distinction of both seismic phases of activity andinactivity, the index GR signifying a greater area about the point ofinvestigation and S₁,GR the SEISMOLAP₋₋ 1 parameter established for thisarea.

Where applied to the Sapanca quake of December 1988 a pronounced phaseof inactivity, lasting roughly two weeks up to one to two days prior tophase of activity commencing the quake, materializes. In other caseseven longer phases of inactivity materialized, whereby in part the phaseof inactivity started several months up to a few years prior to themajor earthquake occurence. This duration relates to the strength of thecoming, more severe quake. In the time development of SEISMOLAP₋₋ 2 suchphases of inactivity can be highlighted particularly well. Theseobservations have resulted in investigations being focused on thecomputation and recording of the SEISMOLAP₋₋ 2 parameter since, inprinciple, this is in a position to forecast the earthquake occurence afew weeks or months before, or in the case of very severe quakes, evenyears before.

The method employed hitherto has the drawback that it hadn't yet takeninto account that the locations at which the values of S₁ or S₂ arecomputed and monitored may exhibit a differing basic activity. At alocation having a higher seismic basic activity any relative seismicinactivity recorded there has a different meaning to a relative seismicinactivity recorded at a location exhibiting a low seismic basicactivity. Hitherto the method failed to take these locationaldifferences into account which may result in a false prediction. In anycase, predicting was unreliable due to this.

It is thus the object of the present invention to define an improvedmethod of monitoring the parameters of seismic activity in a regionbeing investigated, and a device for implementing this method which moreparticularly permits enhanced reliability in earthquake prediction. Moreparticularly it is the object of the present invention to define amethod of monitoring the parameters of seismic activity which is capableof furnishing an improved means of comparing the SEISMOLAP₋₋ 2parameters recorded locationally.

This object is achieved by the subject matters of claims 1 and 11.Advantageous embodiments of the invention are set forth in thesub-claims.

The method in accordance with the invention achieves the object as citedabove more particularly in such a manner that the values SEISMOLAP₋₋ 1or SEISMOLAP₋₋ 2 are no longer investigated as an indicator for animminent major occurence (earthquakes of strength M>4) but, instead, theprobability of the SEISMOLAP values occurring is used. Extreme values ofSEISMOLAP₋₋ 2 as are observed for example in the case of a seismicinactivity then occur as signalizable values with extremely lowprobability. This ensures that the locational values can be comparedeven when at the various locations a different basic activity of themicroquake activity exists. The probability can be determined in themethod e.g. from adapting the "Pearson type 3 probability distribution"to the frequency distribution of the SEISMOLAP values. This probabilitydistribution is often made use of in water management is modellingextreme water levels.

The method requires that for each location, for which a prediction is tobe made, an adaptable frequency distribution exists, i.e. that for eachsuch location SEISMOLAP values have been recorded over a sufficientlylong period in time so that SEISMOLAP values can be derived and afrequency distribution formed from these SEISMOLAP values can be adaptedby a plot of a Pearson type 3 probability distribution.

The invention will now be described in more detail with reference to theattached FIGS. in which:

FIG. 1 plots the probability distributions of the SEISMOLAP₋₋ 1parameter for two different points of investigation; and

FIG. 2 illustrates establishing the SEISMOLAP₋₋ 1 parameter as knownfrom prior art.

In accordance with the invention at least one of the parametersSEISMOLAP₋₋ 1 or SEISMOLAP₋₋ 2 is firstly established over apredetermined period of time at a location of interest in the regionbeing investigated, from which corresponding frequency distributions areplotted.

For this purpose a time period of n-times (n>1) of the time window usedin calculating the SEISMOLAP values is selected. Typically n equals 3 sothat for a time window of e.g. 100 days SEISMOLAP data of 300 days areneeded for plotting the frequency distribution and for subsequentlyadapting a Pearson type 3 probability curve.

Subsequently, for the corresponding occurence a probability value isestablished from each parameter by adapting a probability distributionto the frequency distribution.

Preferably a strongly asymmetrical probability distribution is selectedwhich permits adapting to extreme values occurring relatively seldomly.This is satisfied e.g. by the Pearson type 3 probability distribution inaccordance with the following equation (3)

    P(ε)=(1/Γ(m))∫.sub.0.sup.ε α.sup.m-1 e.sup.-α dα                                   (3)

ε=(2/C_(SS)){(S-μ_(S))/σ_(S) +2/C_(SS) }>0

m=(4/C_(SS) ²)>0μ_(S) =(1/ι)Σ_(i=o) ^(i-1) S_(i)

σ_(S) =(1/(ι-1)){Σ_(i=o) ^(i-1) (S_(i) -μ_(S))² }^(1/2)

C_(SS) ={ι^(1/2) Σ_(i=0) ^(i-1) (S_(i) -μ_(S))³ }/{(ι-1)^(3/2) σ_(S) ³ }

where s=S₂ (T_(k)) is the instant value of SEISMOLAP₋₋ 2 at the point intime T=T_(k), the value to which a probability value is to be assigned,S_(i) =S₂ (T_(k)) are values of SEISMOLAP₋₋ 2 at the point in timeT=T_(k-i) in the statistical time window, 1 is the number of values ofSEISMOLAP₋₋ 2 in this window, Γ(m) is the gamma function.

In equation (3), P(ε) is the probability of the value e being exceeded.If C_(SS) is positive, then P(ε) is identical to the probability of theSEISMOLAP₋₋ 2 value s being exceeded. In this case 1/P(ε) (correspondingto 1/probability) is a direct measure for seismic inactivity. Thus, thehigher is 1/P(ε), the less is the probability that a very low level ofseismic activity can be considered as being normal. If C_(SS) isnegative then 1/(1-P(ε)) must be used instead.

From each probability value a further parameter SEISMOLAP₋₋ 3 isestablished: ##EQU3##

This probability value of at least one parameter (as an alternative tothe description as regards SEISMOLAP₋₋ 2 the probability value may alsobe determined with respect to SEISMOLAP₋₋ 1) is then stored for thepoint of investigation (X, Y, Z) of interest and for every furtherinvestigation time (T_(k)).

In FIG. 1 probability distributions of the parameter SEISMOLAP₋₋ 1 fortwo different locations are represented by curves A and B (dashed) byway of example. Since the parameter SEISMOLAP₋₋ 2 in principlerepresents the reciprocal value of SEISMOLAP₋₋ 1, correspondingprobability curves can also be plotted for SEISMOLAP₋₋ 2. It is evidentfrom FIG. 1 that the location corresponding to the plot A shows arelative low basic level of seismic activity, since the maximum of itsprobability distribution, as compared to plot B, lies at a relative lowvalue of SEISMOLAP₋₋ 1. Accordingly, also phases of relative seismicinactivity are considerably more probably at location A so thatconcluding an imminent activity is not necessarily certain as yet from alow value of SEISMOLAP₋₋ 1, whereas in the case of plot B the maximum ofthe probability distribution is shifted to higher values ofSEISMOLAP₋₋ 1. The location B thus exhibits a relative basic level ofseismic activity. Accordingly, at location B phases of relative seismicactivity are considerably more probable than at location A so that hereconcluding an imminent activity is not necessarily certain as yet from ahigh value of SEISMOLAP₋₋ 1. Thus, for each location of interest it isnot the development in time of the established SEISMOLAP value which isconsidered, but that of the associated probability value establishedfrom the probability distribution. This probability value is comparablelocationally so that the comparability of the predicting method isimproved overall locationally and thus the spatial position of theanomaly can be determined more accurately.

In conclusion the occurence of activities leading up to earthquakes areeach recorded and/or signalized in the form of microquake clustersand/or seismic inactivity for the point being investigated when theprobability value for a sequence of points in time exhibit a significantchange (falling or rising tendency). Signalizing may be triggered in anysuitable form by means for outputting optical or acoustical signals orother display indications as soon as the probability value violates aspecific threshold in response to the region being investigated in eachcase or exceeds a corresponding threshold of a defined rate of changeper unit of time.

The method in accordance with the instant invention has, apart from theadvantage of permitting the results,to be compared spatially and thusbetter pin-pointing the location, also the advantage that thesignal-to-noise ratio is improved. In addition to this the result isachieved more or less irrespectively of the average number of quakesoccurring in the selected space window.

The method in accordance with the invention has since been successfullytested with the data obtained from more than 100 earthquake events in"postcasting", including disasters such as Spitak (Armenia, 1988), LomaPrieta (US, 1989), Landers (US, 1992), Hokkaido (Japan, 1993),Northridge (USA, 1994), Kobe (Japan, 1995) and Aigon (Greece, 1995).

As a special modification of the method in accordance with the inventiona further window can be introduced for a fifth dimension, namely themagnitude of the microquakes, in the same way as for the other fourdimensions (space and time). This modification makes it possible todetect the SEISMOLAP anomalies pointing to quakes even when they occuronly in certain magnitude bands. For this purpose the point ofinvestigation is assigned a magnitude window about the centralmagnitude. Likewise, each microquake is assigned a window about itssensed magnitude, it being usual that windows of the same size areselected for both kinds. The overlap of the two windows then determinesthe contribution of a specific band (or interval or range) of magnitudeto the SEISMOLAP parameter in the same way as for the other fourdimensions.

As an alternative, processing can be done to advantage also merely witha lower magnitude threshold which permits, for example, the activitiesleading up to very severe occurrences (also to be viewed in largishmicroquake magnitudes) to be separated from those of occurences whichare not so severe (to be viewed merely in smallish microquakemagnitudes). A corresponding separation may also be made in a similarway by suitably selecting the two other free parameters of the method(time window and space window).

A device for implementing the method in accordance with the inventionuses e.g. a network of 15 seismographs which are suitably arranged inthe region under investigation. The region of investigation has, forexample, a size of approx. 200,100 km. The seismographs sense theseismic vibrations and supply the vibration values to a system whichdetermines point in time, position and strength of the microquakes andthen realizes the method as described. This system may be moreparticularly a computer or memory unit comprising means for implementingeach of the steps of the method in accordance with the invention and,where appropriate, signalizing means.

I claim:
 1. A method of monitoring parameters of changes in space andtime of the seismic activity at predetermined points of investigation(X, Y, Z) and times of investigation (T_(k)) in a region ofinvestigation, in the method of which the location and time of quakes ofrelative low strength (microquakes) are sensed in said region ofinvestigation and the seismic activity of the microquakes is quantifiedby means of at least one parameter (S₁, S₂) at said point ofinvestigation (X, Y, Z) and at said times of investigation T_(k)), eachof at least one of said parameter (S₁, S₂) being a time-variable measurefor the accumulation in space and time of said microquakes and/or theseismic inactivity at said point of investigation (X, Y, Z)characterized by the stepsa) sensing at least the one parameter (S₁, S₂)over a predetermined period of time and generating a frequencydistribution of said at least one parameter for each point ofinvestigation; b) establishing a probability value corresponding to theoccurence of each parameter by adapting a probability distribution tosaid frequency distribution; c) establishing and storing saidprobability value of the at least one parameter for said point ofinvestigation (X, Y, Z) at each further time of investigation T_(k)); d)recording and/or signalizing the occurence of each activity leading toan earthquake in the form of microquake clustering and/or seismicinactivity for the point of investigation, when said probability valueexhibits a significant change, more particularly of a falling or risingtendency for a sequence of points in time.
 2. The method as set forth inclaim 1, at least one parameter being formed by a first parameter(SEISMOLAP₋₋ 1, S₁) and/or a second parameter (SEISMOLAP₋₋ 2, S₂), saidsecond parameter (SEISMOLAP₋₋ 2, S₂) being the reciprocal value of saidfirst parameter (SEISMOLAP₋₋ 1, S₁).
 3. The method as set forth in claim1, at least one parameter being formed by a first parameter (SEISMOLAP₋₋1, S₁) and/or a second parameter (SEISMOLAP₋₋ 2, S₂), said secondparameter (SEISMOLAP₋₋ 2, S₂) being the reciprocal value of a parameter(SEISMOLAP₋₋ 1_(G), S₁,G) corresponding to said first parameter(SEISMOLAP₋₋ 1, S₁) when the latter is established for a reference areagreater in comparison to the region being investigated.
 4. The method asset forth in claim 2, said first parameter (SEISMOLAP₋₋ 1, S₁) beingdetermined such that to each microquake is assigned a window in spaceand time and the overlaps of the windows of each microquake are computedand summed together with a window defined in space and time at saidpoint of investigation.
 5. The method as set forth in claim 4, said timeperiod in said step a) of the method corresponding to a multiple, moreparticularly, 3-times the length of said time window.
 6. The method asset forth in claim 1, said probability distribution being a stronglyasymmetrical probability distribution which is adaptable to extremevalues occurring relatively seldomly.
 7. The method as set forth inclaim 6, said probability distribution being a Pearson type 3probability distribution.
 8. The method as set forth in claim 4, saidmagnitude of said space window being selected in response to thestrength of said microquake.
 9. The method as set forth in claim 4, saidpoint of investigation and each said microquake is additionally assigneda magnitude window and said overlap of said magnitude window of saidpoint of investigation being entered in the calculation of said firstparameter with each magnitude window of said microquakes.
 10. The methodas set forth in claim 1, said point of investigation being assigned alower magnitude threshold and only microquakes being taken into accountwhose magnitude lie above said threshold.
 11. A device for implementinga method according to claim 1 characterized by a network of devices fordetecting the seismic activity in said region being investigated and bya computer system for computing said at least one parameter, saidfrequency distribution and for adapting said probability distribution tosaid frequency distribution.